Background: The COVID-19 pandemic has caused tremendous impact on global health and economics. The impact in African countries has not been investigated through fitting epidemic model to the reported COVID-19 deaths.Method: We downloaded data for the twelve most-affected countries with the highest cumulative COVID-19 deaths to estimate the time-varying effective reproduction number (B) and infection attack rate (IAR). We developed a simple epidemic model and fitted the model to reported COVID-19 deaths in 12 African countries, using iterated filtering and allowing flexible transmission rate. Results: We found high heterogeneity in the case-fatality rate across countries, which may be due to different reporting or testing efforts. We found that South Africa, Tunisia, and Libya were hit hardest with a relatively higher a and infection attack rate Conclusion: To effectively control the spread of COVID-19 epidemics in Africa, there is a need to consider other mitigation strategies (such as improvement in socio-economic wellbeing, health care system, water supply, awareness campaigns).
Objectives. Serological surveys were used to infer the infection attack rate in different populations. The sensitivity of the testing assay, Abbott, drops fast over time since infection which make the serological data difficult to interpret. In this work, we aim to solve this issue. Methods. We collect longitudinal serological data of Abbott to construct a sensitive decay function. We use the reported COVID-10 deaths to infer the infections, and use the decay function to simulate the seroprevalence and match to the reported seroprevalence in 12 Indian cities. Results. Our model simulated seroprevalence match the reported seroprevalence in most (but not all) of the 12 Indian cities we considered. We obtain reasonable infection attack rate and infection fatality rate for most of the 12 Indian cities. Conclusions. Using both reported COVID-19 deaths data and serological survey data, we infer the infection attack rate and infection fatality rate with increased confidence.
The COVID-19 pandemic poses a serious threat to global health, and one of the key epidemiological factors that shape the transmission of COVID-19 is its serial interval (SI). Although SI is commonly considered following a probability distribution at a population scale, slight discrepancies in SI across different transmission generations are observed from the aggregated statistics in recent studies. To explore the change in SI across transmission generations, we develop a likelihood-based statistical inference framework to examine and quantify the change in SI. The COVID-19 contact tracing surveillance data in Hong Kong are used for exemplification. We find that the individual SI of COVID-19 is likely to shrink with a rate of 0.72 per generation and 95%CI: (0.54, 0.96) as the transmission generation increases. We speculate that the shrinkage in SI is an outcome of competition among multiple candidate infectors within a cluster of cases. The shrinkage in SI may speed up the transmission process, and thus the nonpharmaceutical interventive strategies are crucially important to mitigate the COVID-19 epidemic.
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